Systems and methods for embolism prediction using embolus source and destination probabilities

ABSTRACT

Systems and methods are disclosed for determining a patient risk assessment or treatment plan based on emboli dislodgement and destination. One method includes receiving a patient-specific anatomic model generated from patient-specific imaging of at least a portion of a patient&#39;s vasculature; determining or receiving a location of interest in the patient-specific anatomic model of the patient&#39;s vasculature; using a computing processor for calculating blood flow through the patient-specific anatomic model to determine blood flow characteristics through at least the portion of the patient&#39;s vasculature of the patient-specific anatomic model downstream from the location of interest; and using a computing processor for particle tracking through the simulated blood flow to determine a destination probability of an embolus originating from the location of interest in the patient-specific anatomic model, based on the determined blood flow characteristics.

RELATED APPLICATION(S)

This application claims priority to U.S. Provisional Application No.62/103,230 filed Jan. 14, 2015, the entire disclosure of which is herebyincorporated herein by reference in its entirety.

FIELD OF THE DISCLOSURE

Various embodiments of the present disclosure relate generally todisease assessment and related methods. More specifically, particularembodiments of the present disclosure relate to systems and methods fordisease assessment using predictions regarding embolism dislodgement anddestination.

BACKGROUND

Emboli are intravascular masses that travel through the bloodstream.Clinical consequences may occur when an embolus lodges in a bloodvessel, causing a blockage of the vessel and obstructing blood flow. Thelevel of harm introduced by an embolism may be related to the locationin the patient's vasculature where an embolus lodged. In other words, anembolus destination may determine the impact of that embolus on apatient's health. For example, a sizable embolus entering the lungs maycause a life-threatening pulmonary embolism. An embolus lodging in thebrain may cause a stroke. By contrast, emboli entering vessels inmuscles or the liver may have less impact on a patient's body.

Thus, a desire exists to better predict embolus destination, orlocations in a vasculature that may be vulnerable to an embolism lodgingat that location. By better understanding emboli destinations,practitioners may better predict the degree of harm that the emboli mayinflict on a patient. Meanwhile, a desire also exists for identifyingsource locations of emboli causing harmful embolisms, so that treatmentsmay be targeted at those source locations.

The foregoing general description and the following detailed descriptionare directed to overcoming one or more of the challenges describedabove. The general description and detailed description are exemplaryand explanatory only and are not restrictive of the disclosure.

SUMMARY

According to certain aspects of the present disclosure, systems andmethods are disclosed for disease assessment using predictions regardingembolism dislodgement and destination.

One method includes: receiving a patient-specific anatomic modelgenerated from patient-specific imaging of at least a portion of apatient's vasculature; determining or receiving a location of interestin the patient-specific anatomic model of the patient's vasculature;using a computing processor for calculating blood flow through thepatient-specific anatomic model to determine blood flow characteristicsthrough at least the portion of the patient's vasculature of thepatient-specific anatomic model downstream from the location ofinterest; and using a computing processor for particle tracking throughthe simulated blood flow to determine a destination probability of anembolus originating from the location of interest in thepatient-specific anatomic model, based on the determined blood flowcharacteristics.

In accordance with another embodiment, a system is disclosed fordetermining a patient risk assessment or treatment plan based on embolidislodgement and destination: a data storage device storing instructionsfor simulating or optimizing hemodialysis access; and a processorconfigured for: receiving a patient-specific anatomic model generatedfrom patient-specific imaging of at least a portion of a patient'svasculature; determining or receiving a location of interest in thepatient-specific anatomic model of the patient's vasculature; using acomputing processor for calculating blood flow through thepatient-specific anatomic model to determine blood flow characteristicsthrough at least the portion of the patient's vasculature of thepatient-specific anatomic model downstream from the location ofinterest; and using a computing processor for particle tracking throughthe simulated blood flow to determine a destination probability of anembolus originating from the location of interest in thepatient-specific anatomic model, based on the determined blood flowcharacteristics.

In accordance with another embodiment, a non-transitory computerreadable medium is disclosed for use on a computer system containingcomputer-executable programming instructions for performing a method ofdetermining a patient risk assessment or treatment plan based on embolidislodgement and destination, the method comprising: receiving apatient-specific anatomic model generated from patient-specific imagingof at least a portion of a patient's vasculature; determining orreceiving a location of interest in the patient-specific anatomic modelof the patient's vasculature; using a computing processor forcalculating blood flow through the patient-specific anatomic model todetermine blood flow characteristics through at least the portion of thepatient's vasculature of the patient-specific anatomic model downstreamfrom the location of interest; and using a computing processor forparticle tracking through the simulated blood flow to determine adestination probability of an embolus originating from the location ofinterest in the patient-specific anatomic model, based on the determinedblood flow characteristics.

Another method includes: receiving a patient-specific anatomic model ofat least a portion of a patient's vasculature; determining or receivinga destination location of interest in the patient-specific anatomicmodel, wherein the destination location of interest is an embolusdestination or an embolism location in the patient's vasculature;determining a destination probability of an embolus in the patient'svasculature lodging at the destination location of interest; anddetermining a source of the embolus in the patient's vasculature, basedon the destination probability of the embolus.

Yet another method includes: receiving a patient-specific anatomic modelof a patient's vasculature; receiving an anatomic location associatedwith a vascular treatment or a vascular procedure; determining orreceiving one or more blood flow characteristics through the anatomiclocation associated with the vascular treatment or the vascularprocedure; determining a destination probability of an embolus travelingthrough the patient's vasculature, based on the one or more determinedblood flow characteristics through the anatomic location associated withthe vascular treatment or the vascular procedure; and determining avulnerable embolism location in the patient's vasculature based on thedetermined destination probability.

Yet another method includes: receiving a medical condition or disease ofinterest; determining a vessel associated with causing the medicalcondition or disease of interest; determining a probability of anembolus lodging in the vessel associated with causing the medicalcondition or disease of interest, where the probability is computed orretrieved from a database; and determining a vascular source of theembolus, based on the probability of the embolus lodging in the vesselassociated with causing the medical condition or disease of interest.

Additional objects and advantages of the disclosed embodiments will beset forth in part in the description that follows, and in part will beapparent from the description, or may be learned by practice of thedisclosed embodiments. The objects and advantages of the disclosedembodiments will be realized and attained by means of the elements andcombinations particularly pointed out in the appended claims.

It is to be understood that both the foregoing general description andthe following detailed description are exemplary and explanatory onlyand are not restrictive of the disclosed embodiments, as claimed.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of this specification, illustrate various exemplary embodiments andtogether with the description, serve to explain the principles of thedisclosed embodiments.

FIG. 1 is a block diagram of an exemplary system and network for diseaseassessment using predictions regarding embolism dislodgement anddestination, according to an exemplary embodiment of the presentdisclosure.

FIG. 2A is a flowchart of an exemplary method of determining embolicirculatory destination probabilities, according to an exemplaryembodiment of the present disclosure.

FIG. 2B is a flowchart of an exemplary method of identifying sourcelocations for emboli, according to an exemplary embodiment of thepresent disclosure.

FIG. 3A is a flowchart of an exemplary method of determiningdestination(s) in cerebral vessels for an embolus dislodged from alocation in a patient's vasculature, according to an exemplaryembodiment of the present disclosure.

FIG. 3B is a flowchart of an exemplary method of determining andevaluating source locations of embolisms in a patient's cerebralvasculature, according to an exemplary embodiment of the presentdisclosure.

FIG. 3C is a flowchart of an exemplary method of determining blood flowcharacteristics to compute a circulatory destination probability of adislodged embolus for cerebral-related risks, according to an exemplaryembodiment of the present disclosure.

FIG. 4A is a flowchart of an exemplary method of determiningdestination(s) in peripheral vessels for an embolus dislodged from alocation in a patient's vasculature, according to an exemplaryembodiment of the present disclosure.

FIG. 4B is a flowchart of an exemplary method of determining andevaluating source locations of embolisms in a patient's peripheralvasculature, according to an exemplary embodiment of the presentdisclosure.

FIG. 4C is a flowchart of an exemplary method of determining blood flowcharacteristics and circulatory destination probability of a dislodgedembolus for peripheral-related risks, according to an exemplaryembodiment of the present disclosure.

FIG. 5A is a flowchart of an exemplary method of assessing or assigninga risk of potential emboli dislodgement associated with an invasiveprocedure, according to an exemplary embodiment of the presentdisclosure.

FIG. 5B is a flowchart of an exemplary method of determining andevaluating source locations of embolisms associated with invasiveprocedures, according to an exemplary embodiment of the presentdisclosure.

FIG. 5C is a flowchart of an exemplary method of determining blood flowcharacteristics to compute circulatory destination probability of adislodged embolus related to invasive procedures, according to anexemplary embodiment of the present disclosure.

DESCRIPTION OF THE EMBODIMENTS

Reference will now be made in detail to the exemplary embodiments of thedisclosure, examples of which are illustrated in the accompanyingdrawings. Wherever possible, the same reference numbers will be usedthroughout the drawings to refer to the same or like parts.

An embolus dislodged from atherosclerotic plaques may cause clinicalcomplications, depending on the source and destination of the embolus inthe circulatory system. In other words, patient risk of heart diseaseand attack may be based, in part, on embolus destination, e.g., in thecoronary arteries. The size and destination of an embolus may indicatethe extent to which the embolus may harm the patient. For example, deepvein thrombosis may cause a life-threatening pulmonary embolism if asizable embolus enters the lungs. As another example, embolism inpatients may cause stroke or transient ischemic attack (TIA). Similarly,early microembolism after carotid endarterectomy may relate topostoperative cerebral ischemia. Stroke may also be caused byembolization from the aortic arch or other places in the vasculature,including the left atrial appendage of the heart.

Thus, a desire exists to better predict embolus destination. The presentdisclosure includes methods to determine embolus destination based onthe location of embolic source, vascular anatomy, blood flowcharacteristics, and/or circulatory system.

This disclosure includes systems and methods for assessing the impact ofan embolus, as a function of the source of the embolus and a patient'scirculatory system. For example, this disclosure describes systems andmethods for predicting the circulatory destination probability of anembolus dislodged from a specified location, including the heart (e.g.,left atrium, left atrium with atrial fibrillation, aortic valve, mitralvalve, left ventricular aneurysms, prosthetic aortic or mitral valves,abdominal aorta, carotid, coronary arteries, veins, etc.) to assess (i)the impact of an embolus on cerebral-related risks (e.g., cognitiveimpairment, stroke, TIA), (ii) the impact of an embolus onperipheral-related risks (e.g., pulmonary embolism), and/or (iii) a riskof potential emboli dislodgement associated with invasive procedures.

Thus, this disclosure includes systems and methods for assessing theimpact of embolism on patient risk and evaluating therapeutic optionsbased, at least in part, on the impact of embolism. The disclosurefurther includes systems and methods for evaluating therapeutic optionsbased on the assessments. For example, this disclosure may includemethods to identify culprit embolic sources for treatment. Identifyingor predicting source locations of emboli may provide treatmentrecommendations targeted to locations where dislodged emboli may cause aharmful embolism.

Referring now to the figures, FIG. 1 depicts a block diagram of anexemplary system 100 and network for disease assessment usingpredictions regarding embolism dislodgement and destination, accordingto an exemplary embodiment. Specifically, FIG. 1 depicts a plurality ofphysicians 102 and third party providers 104, any of whom may beconnected to an electronic network 101, for example, the Internet,through one or more computers, servers, and/or handheld mobile devices.Physicians 102 and/or third party providers 104 may create or otherwiseobtain images of one or more patients' anatomy. The physicians 102and/or third party providers 104 may also obtain any combination ofpatient-specific information, including age, medical history, bloodpressure, blood viscosity, patient activity or exercise level, etc.Physicians 102 and/or third party providers 104 may transmit theanatomical images and/or patient-specific information to server systems106 over the electronic network 101. Server systems 106 may includestorage devices for storing images and data received from physicians 102and/or third party providers 104. Server systems 106 may also includeprocessing devices for processing images and data stored in the storagedevices. For the present disclosure, “patient” may refer to anyindividual of interest.

FIG. 2A depicts a flowchart of a general embodiment for identifyingemboli source locations, circulatory destination probabilities, andlocations vulnerable to embolism. The flowchart of FIG. 2A may furtherdepict evaluating patient risk or treatment options associated with thecirculatory destination probabilities and locations vulnerable toembolism. FIG. 2B depicts a flowchart of a general embodiment forfinding embolic sources for one or more patient conditions or risks.FIGS. 3A-5B depict exemplary applications of the methods shown in FIGS.2A and 2B. For example, FIGS. 3A-3C depict flowcharts for a specificembodiment for predicting cerebral-related risks and evaluatingtreatment options associated with the cerebral-related risks. FIGS.4A-4C depict flowcharts for a specific embodiment for predictingperipheral-related risks and evaluating treatment options associatedwith the peripheral-related risks. FIGS. 5A-5C depict flowcharts for aspecific embodiment for assessing a risks (e.g., of potential embolidislodgement or embolism) associated with an invasive procedure. Theexemplary methods of the figures may be performed or used individually,or in any combination. Any or all the steps of the exemplary methods maybe performed using a computing processor.

FIG. 2A is a flowchart of an exemplary method 200 of determining embolisource locations, circulatory destination probabilities, and vessellocations vulnerable to embolism, according to an exemplary embodiment.The method of FIG. 2A may be performed by server systems 106, based oninformation, images, and data received from physicians 102 and/or thirdparty providers 104 over electronic network 101.

In one embodiment, step 201 may include receiving a model of a patient'svasculature and physiologic characteristics (e.g., in an electronicstorage medium). For example, the vascular model may include a portionof the patient's vasculature, or the patient's entire circulatorysystem. Various portions of the vascular model may be at imaged and/ormodeled varying levels of detail. For example, in a case involving amodel of the patient's entire circulatory system, prioritized portionsor portions of interest in the patient's circulatory system may bemodeled in greater detail. Remaining portions of the model may beinferred. One such exemplary scenario may include modeling portions ofinterest for the received patient vascular model as 3D geometric,anatomic models while modeling remaining portions of the receivedpatient vascular model as reduced order models. The vascular model mayinclude a composite of various models, reflecting, for instance,variations in geometry, psychological response, boundary conditions,and/or physics-based variations in blood flow for respective location(s)in the vasculature.

In one embodiment, step 203 may include receiving one or more locationsof interest in the patient (e.g., a plaque, pathological area, locationof possible vascular shedding, etc.) via invasive and/or noninvasiveimaging, for storage in an electronic storage medium. Step 203 may alsoinclude identifying one or more locations of interest using a computingprocessor. For example, step 203 may include identifying a plaque, apathological area, and/or a location of possible vascular shedding inthe received model of the patient's vasculature and designating theidentified plaque, pathological area, and/or location of possiblevascular shedding as one or more locations of interest. In oneembodiment, the one or more locations of interest may be stored withinan electronic storage drive.

In one embodiment, step 205 may include computing circulatorydestination probability in order to determine potential destination(s)for an embolus dislodged from one or more locations of interest. Forexample, step 205 may include determining one or more blood flowcharacteristics and using the determined blood flow characteristic(s) todetermine the circulatory destination probability of an embolusdislodged from the identified locations of interest of the patient'svasculature. The blood flow characteristics may be determined based onthe patient's vasculature and the received physiologic characteristics(e.g., from step 201). For example, the blood flow characteristics maybe determined by simulating blood flow through at least a portion of themodel of the patient's vasculature. The determinations of circulatorydestination probability may be calculated via particle tracking, using acomputing processor (as described in further detail, for example, atstep 309 of FIG. 3A, step 409 of FIG. 4A, and step 509 of FIG. 5A).

In one embodiment, step 207 may include determining locations vulnerableto embolism, based on the computed destination probabilities for a givenlocation in the patient's modeled vasculature (e.g., a location ofinterest). The circulatory destination probability may indicate adestination/target location in the patient's vasculature where thedislodged embolus (e.g., from step 203) may lodge, as well as thelikelihood that the dislodged embolus would lodge at that particulardestination. In other words, the output of step 207 may include variouslocations (e.g., destinations) in a patient's vasculature where anembolism may form, given the identified locations of interest (e.g., ofstep 203). In one embodiment, the various locations may compriselocations vulnerable to embolism (e.g., vulnerable locations in apatient's vasculature).

In a further embodiment, step 207 may include ranking or selectinglocations vulnerable to embolism, from the various locations (e.g.,destinations) in a patient's vasculature where an embolism may form. Forexample, step 207 may include identifying a location vulnerable in apatient's vasculature vulnerable to embolism where the destinationprobability of the location exceeds a predetermined threshold. Step 207may include identifying a threshold destination probability of alocation in the patient's vasculature. As an example, step 207 maydesignate a destination probability of 50% as a threshold, such that adestination probability that exceeds 50% may cause an associatedlocation to be identified as a “vulnerable location.” For instance,location A may be associated with a 56% likelihood of embolism, whilelocation B may be associated with a 30% likelihood of embolism. Step 207may include designating location A as a “vulnerable location” whilelocation B is not. Alternately or in addition, vulnerable locations maybe designated as locations in a vessel most vulnerable to embolism orwith the highest likelihoods of embolism. For example, the threelocations in a vessel most vulnerable to embolism or locations of apatient's vasculature with the top three destination probabilities maybe designated as “vulnerable locations.”

In one embodiment, step 207 may further include associating anidentified vulnerable location (of embolism) with the location ofinterest (e.g., source location for the dislodged emboli). In otherwords, step 207 may further include determining, for a location in apatient's vasculature (e.g., an embolic source), a probability ofembolism associated with emboli dislodging from the location.

In one embodiment, step 209 may include storing, outputting, and/orgenerating a representation of vulnerable embolism locations andassociated embolic sources, e.g., to an electronic storage medium. In afurther embodiment, step 211 may include outputting a patient risk of anevent associated with the computed risk of embolism and/or thedetermined vulnerable embolism location(s). For example, computingpatient risk for the event of a stroke may include one or more of thefollowing: imaging at least a portion of the patient's aortic arch,great vessels, carotid artery, vertebral, and/or intracranialcirculation, identifying, e.g., from imaging, plaques (e.g., at acarotid bifurcation), assessing a degree of stenosis, assessing plaquecharacteristics, assessing plaque composition, determining anassociation between a risk of stroke and a location of stroke (e.g.,from an embolus). A risk of stroke and/or location of stroke (embolus)may be calculated by correlating risk to actual occurrences ofstroke/TIA, diffusion weighted maps of MRI (e.g., to identifysymptomatic stroke and areas of ischemia/infarction of a brain (thatcould be asymptomatic), etc.), etc. In another embodiment, computingpatient risk for stroke may include calculating blood flow patternsand/or predicting vulnerable locations in a patient's anatomy forembolism/stroke.

Machine learning of plaque severity and/or location in conjunction withdistribution of stroke (e.g., from MRI and head CTA) may enhancepredictive certainty. In some cases, patient risk of an embolism-relatedevent may be inferred from the risk of an embolism. In one embodiment,step 211 may include estimating the patient risk of an embolism-relatedevent using a machine-learning based prediction model derived fromclinical data on the relationship between detected emboli and actualclinical events. For instance, an exemplary event associated with therisk of embolism may include stroke. A vulnerable embolism locationwithin the brain may relate to a high patient risk of stroke, whereas apatient may have a low risk of stroke if the computed circulatorydestination probability indicates a low probability that an emboli fromthe patient's location of interest (e.g., from step 203), would lodge inthe patient's cerebral vasculature.

In one embodiment, method 200 may include determining one or moredestinations of interest (e.g., target location(s)) in the patient'svasculature. For example, destinations of interest may include locationsin the circulatory system where embolism presence would be particularlyharmful, e.g., one or more of the aorta, carotid artery, variousperipheral vessels, etc. Determining the destinations of interest mayinclude receiving and/or identifying the destinations of interest. Then,method 200 may include calculating a circulatory destinationprobability, in particular, for an embolus dislodged from a location ofinterest and traveling to one of the one or more destinations ofinterest. Such an embodiment may include an analysis in method 200particularly providing determinations of risk of certain eventsassociated with embolism. For example, a patient's lungs or the brainmay serve as destinations of interest in a case where peripheral vessels(e.g., peripheral veins) may be defined as sources of interest andmethod 200 may be used to provide an assessment destination risk forembolization to the lungs (pulmonary embolism) or to the brain (if thepatient has a patent foramen ovale in the heart). Then, method 200 mayinclude calculating source probability of the peripheral vessels,destination probability of the lungs and brain, and/or the patient'srisk of pulmonary embolism, rather than performing a more comprehensiveassessment for several locations vulnerable to embolism or for patientrisk of various different events associated with embolism.

FIG. 2B is a flowchart of an exemplary method 220 of identifying sourcelocations for emboli, according to an exemplary embodiment. The methodof FIG. 2B may be performed by server systems 106, based on information,images, and data received from physicians 102 and/or third partyproviders 104 over electronic network 101.

In one embodiment, step 221 may include receiving (and/or determining)one or more destinations of interest in a patient's vasculature. Adestination of interest in a patient's vasculature may relate to alocation in a patient's vasculature where an embolism may impact apatient's health, or create a risk of harm to the patient.

In one embodiment, step 223 may include determining a computeddestination probability (e.g., from step 205 of method 200) associatedwith at least one destination of interest of the one or moredestinations of interest. For example, step 223 may include retrieving,from stored destination probabilities (e.g., from step 209 of method200), a destination probability associated with the at least onedestination of interest. In some cases, multiple destinationprobabilities may be associated with a destination of interest, sinceemboli may be associated with various sources, each corresponding to adifferent destination probability.

In one embodiment, step 225 may include determining an embolic sourceassociated with the computed destination probability (e.g., from step205 or step 223) for the at least one destination of interest of the oneor more destinations of interest of step 221. The embolic source mayinclude a location in the model of the patient's vasculature comprisingat least one location (or a portion of at least one location) of the oneor more of the locations of interest (e.g., from step 203). For example,a location of interest may include a segment of the patient's modeledvasculature. The embolic source may include a particular location orsub-section of the segment of the patient's modeled vasculature. Forexample, emboli that may theoretically lodge at the destination ofinterest may arrive from several source locations within the patient'svasculature. Step 225 may include determining sources of emboli mostlikely to arrive at a destination of interest, given the patient'scirculatory pattern.

In one embodiment, step 227 may include storing, outputting, and/orgenerating a representation of a destination of interest (e.g., avulnerable embolism location) with one or more associated embolicsources, e.g., to an electronic storage medium.

In a further embodiment, step 229 may include outputting a patient riskof an event associated with a risk of embolism from the computeddestination probability. Step 229 may further include generatingtreatment recommendations associated with one or more of the determinedembolic sources (e.g., from step 225). For example, if the highestprobability of embolic source is determined to be at the left atrialappendage, step 229 may include generating a recommendation of a leftatrial appendage closing procedure in order to prevent a stroke. If theembolic source is a deep vein thrombus in the legs, step 229 may includegenerating a recommendation of a vena cava filter that can trap theembolus before it reaches the lung, thus possibly protecting the patientfrom a fatal pulmonary embolus. If an ulcerating carotid plaque is thesource for cerebral emboli, step 229 may include generating arecommendation of a carotid endarterctomy to attempt to eliminate theembolic source and prevent stroke.

FIGS. 3A-3C depict exemplary methods of predicting cerebral-relatedrisks and evaluating treatment options associated with thecerebral-related risks, according to an exemplary embodiment. Embolisources may include atheromatous plaque in a carotid artery or an aorta,heart chambers with atrial fibrillation, and/or prosthetic heart valves.The presence of microembolisms may correlate with a patient's risk ofstroke, TIA, cognitive impairment, and/or postoperative cerebralischemia. The methods of FIGS. 3A-3C may include identifying likelyembolic sources, e.g., by using computational fluid dynamics (CFD)analyses or simulations applied to patient-specific images of cerebralarteries and other vasculatures. Outputs of methods in FIGS. 3A-3C mayprovide predictions or recommendations for reducing the risk of stroke,TIA, cognitive impairment, and/or postoperative cerebral ischemia.

FIG. 3A is a flowchart of an exemplary method 300 of determiningdestination(s) in cerebral vessels for an embolus dislodged from alocation in patient's vasculature, according to an exemplary embodiment.The method of FIG. 3A may be performed by server systems 106, based oninformation, images, and data received from physicians 102 and/or thirdparty providers 104 over electronic network 101. In one embodiment,steps 301-307 may include receiving various information relating to apatient. For example, step 301 may include receiving the patient'smedical history, including any inherited or acquired hypercoagulablestate that may affect thrombotic risk. For instance, patient medicalhistory may include the patient's family medical history, as well as thepatient's prior history of deep venous thrombosis or pulmonary embolism,factor V Leiden, cancer, and/or recent trauma or surgery. Step 303 mayinclude receiving information on medications that the patient may betaking that may affect thrombotic risk. Examples of such medications mayinclude: Aspirin, Clopidogrel, Coumadin/Warfarin, Heparin, etc.

In one embodiment, step 305 may include receiving the patient'sphysiologic conditions and/or a model of the patient's anatomy (e.g.,including at least a portion of the patient's circulatory system). Themodel of the patient's anatomy may include a representation of thepatient's heart, aortic arch, coronary, carotid, and cerebral arteries,and/or veins. Alternately or in addition, the model of the patient'sanatomy may include a 3D mesh model (e.g., obtained via segmentation ofcardiac and head CT images) and/or a patient-specific cerebral arterymodel combined with a generic circulatory model (e.g., of a coronary,aortic arch, etc.) based on a population average. Patient physiologicconditions may include, for example: age, sex, blood pressure/heart rateunder rest/exercise conditions, physical activity (e.g., exerciseintensity), sedentary time per day, obesity, etc.

In one embodiment, step 307 may include receiving one or more locationsof interest in the patient's anatomy (e.g., a plaque, pathological area,location of possible vascular shedding, etc.). A location of interestmay include a simulated culprit embolic source. In one embodiment, thelocations of interest may be received in an electronic storage medium.Alternately or in addition, step 307 may include identifying one or morelocations of interest in the patient's anatomy. For example, thelocations of interest may be identified via invasive and/or noninvasiveimaging (e.g., CT, MRI, IVUS, transcranial Doppler ultrasound, etc.). Inone exemplary case, step 307 may include identifying the one or morelocations of interest by detecting atherosclerotic plaques in thepatient's vessel(s). Step 307 may further include storing identifiedlocation(s) of interest electronically (e.g., via an electronic storagemedium, RAM, etc.). Information regarding a location of interest in thepatient may include, for example, information on the presence andseverity of atherosclerotic carotid artery disease, intracranialstenosis, cardiac disease, venous disease, and/or arterial dissection inthe patient's anatomy. For instance, presence and severity of cardiacdisease may include information on any heart condition(s), disorders, orirregularities a patient may have, e.g., atrial fibrillation (and leftatrial appendage activity), performance of one or more prosthetic heartvalves, patent foramen ovale, acute myocardial infarction, and/or leftventricular dysfunction.

In one embodiment, step 309 may include determining blood flowcharacteristics (e.g., using computational fluid dynamics (orapproximation)) based on the patient's medical history, medications,physiologic condition(s), and/or anatomy (e.g., as received from steps301-307). Step 309 may further include determining a circulatorydestination probability of a dislodged embolus (e.g., an embolusdislodged from a location of interest, including the patient's aorta,carotid, or heart) based on the determined blood flow characteristics.In one embodiment, determining the circulatory destination probability,based on an embolus source, may include performing Lagrangian particletracking. An exemplary computational fluid dynamics analysis fordetermining blood flow characteristics and circulatory destinationprobability, is described in the method of FIG. 3C.

In one embodiment, step 309 may further include outputting and/orstoring the circulatory destination probability, e.g., to an electronicstorage medium or display. In some instances, the circulatorydestination probability may be stored such that the probability isassociated with the location of interest, wherein the location ofinterest may be identified as a potential source location for anembolus. Furthermore, step 309 may include determining locationvulnerable to embolism, based on the determined circulatory destinationprobabilities.

FIG. 3B is a flowchart of an exemplary method 310 of determining andevaluating emboli source locations in a patient's cerebral vasculature,according to an exemplary embodiment. The method of FIG. 3B may beperformed by server systems 106, based on information, images, and datareceived from physicians 102 and/or third party providers 104 overelectronic network 101.

In one embodiment, step 311 may include receiving one or moredestination locations of interest in the patient's vasculature. Forexample, for cerebral-related risks (e.g., stroke, TIA, cognitiveimpairment, and/or postoperative cerebral ischemia), destinationlocations of interest may include cerebral arteries. In other words,emboli or microemboli presence in cerebral arteries may present risk ofstroke, TIA, cognitive impairment, and/or postoperative cerebralischemia.

In one embodiment, step 313 may include determining stored and/orcomputed circulatory destination probabilities associated with areceived destination location (e.g., of step 309). For example, thereceived destination locations may be identified as locations vulnerableto embolism.

In one embodiment, step 315 may include determining source location(s)associated with a computed circulatory destination probability (e.g., ofstep 313), and thereby associated with a received destination location(e.g., of step 311). For example, step 315 may include retrieving storedembolic sources (e.g., from method 300), based on circulatorydestination probabilities for the one or more destinations of interest.For example, an embolic source may include one or more locations ofinterest (e.g., from step 307).

In one embodiment, step 317 may include generating various outputsincluding, for example, destination/vulnerable location(s) (e.g., ofstep 309) and/or source location(s) (e.g., of step 315). For example,step 317 may include outputting a representation including one or moredestination probabilities in the patient cerebral artery model. In onecase, such a representation of the cerebral artery model may includevisual indication (e.g., highlighting) at vulnerable embolismlocation(s) and/or at embolic source(s) associated with the patient'scerebral or vulnerable embolism location(s). In one embodiment, theoutput cerebral artery model may be stored in an electronic storagemedium. Alternately or in addition, step 317 may include generating arepresentation or display showing selected embolic sources. For example,the representation or display may include a user interface for a user(e.g., a health care provider) to select one or more locations and/orembolic sources in the received model of the patient's anatomy or outputcerebral artery model. The representation or display may then includenumerical or color indicators showing risk of embolism or destinationprobabilities and/or embolic paths. For example, a representation of anembolic path may include a line indicating at least a portion of thejourney of an embolus through the patient's circulatory system as ittravels from a source location to a target location in the patient'svasculature. The outputs of step 317 may be made accessible tophysicians evaluating potential treatments to reduce the patient's riskof stroke or TIA. For example, the treatments may include targetedaction taken at the identified vulnerable locations. For example,targeted treatments may include actions that may reduce risk of strokeor TIA, including carotid endarterectomy and/or carotid stenting. Insuch a scenario, the carotid bifurcation may be a vulnerable location.For instance, the carotid bifurcation as an embolic source may producesymptoms of amaurosis fugax (temporary blindness in one eye) if anembolus tracks to the patient's ophthalmic artery branch from thebifurcation. The carotid bifurcation as an embolic source may produce astroke if the embolus tracks to the middle cerebral artery from thecarotid bifurcation. Another preventative treatment for embolism mayinclude treatments for embolism to the toe, which may cause gangrene ofthe toe. For such cases, an embolus source may include the patient'saortic bifurcation or iliac artery and a treatment may include aorticfemoral bypass surgery or iliac stenting, respectively. Additionally oralternatively, step 317 may include assessing hemodynamic andbiomechanical forces acting on a patient's vessels or plaque in thepatient's vessels. The forces may include sheer stress, drag, tangentialpressure, etc. Such forces, as well as, e.g., the timing, duration, andmagnitude of such forces, may be used to stratify risks and/or determinetreatment options or recommendations. In other words, levels of risk fora patient may be evaluated as a function of various threshold levels orcombinations of hemodynamic force(s) at a location in a patient'svessel, biomechanical force(s) at a location in a patient's vessel,frequency/timing of the force, and/or duration of the force. Forexample, a magnitude of a hemodynamic or biomechanical force on a plaquemay increase a likelihood of an embolus being dislodged. In onescenario, step 317 may include estimating the likelihood of an embolusbeing dislodged from a patient's vessel wall, based on whether themagnitude of hemodynamic or biomechanical force at a location of plaquein the patient's vasculature exceeds a threshold magnitude of force, fora given period of time.

In one embodiment, step 319 may include analysis of the output of step317, e.g., associating destination probabilities with various locationsin the patient cerebral artery model and effects of emboli presence atthe destinations. For example, step 319 may include outputting a patientrisk of stroke, TIA, cognitive impairment, or postoperative cerebralischemia associated with the computed risk of embolism at one or morelocations in the patient cerebral artery model (e.g., based on thedestination probability). Alternately or in addition, step 319 mayinclude calculating and/or displaying a cumulative risk/probability(e.g., of cognitive impairment) over time. For example, step 319 mayinclude calculating the destination probabilities over a span of time,and then outputting a predicted risk/probability based on the collectivecalculations (e.g., by repeating the above steps with multipleiterations to simulate the cumulative effects of an ongoing release ofmicroemboli).

FIG. 3C is a flowchart of an exemplary method 320 of determining bloodflow characteristics and circulatory destination probability of adislodged embolus, in order to predict cerebral-related risks andevaluate treatment options associated with the cerebral-related risks,according to an exemplary embodiment. The method of FIG. 3C may beperformed by server systems 106, based on information, images, and datareceived from physicians 102 and/or third party providers 104 overelectronic network 101.

In one embodiment, step 321 may include computing a velocity field ofblood flow through a portion of the patient's anatomy (e.g., thepatient's heart, coronary, cerebral, carotid, and/or aortic arch).Computing the velocity field of blood flow may include solvingNavier-Stokes equations computationally under the received patientphysiologic conditions. For example, a computational model may includevenous circulation as well as arterial circulation. Venous circulationmay be modeled by including collapsibility of veins due to the effect offorces external to the body (e.g., gravity, external pressure, etc.) orforces internal to the body (e.g., intra-abdominal and/or intra-thoracicpressure from, for instance, respiration, straining, valsalva, etc.).

In one embodiment, step 323 may include simulating the path of adislodged embolus through the patient's circulatory system based on thecomputed velocity field. For example, step 323 may include virtuallyinjecting particles into the blood flow at the received or identifiedpotential embolic sources (e.g., received or identified carotidstenosis, atheromatous plaque in aorta, and/or heart valves from step315).

Step 325 may include determining a trajectory of one or more of theparticles (e.g., by solving the ordinary differential equation of {dotover (x)}(t)=u(x,t); x(t₀)=x₀ using an appropriate numerical method,where u(x,t) is the velocity field and x(t) is the location of particleat time t). The size and number of particles may be determined bynon-invasive imaging (e.g., ultrasound) or estimated by the diseaseseverities of embolic sources.

In one embodiment, step 327 may include determining the destinationprobability of an embolus. For example, step 327 may include computingthe ratio of the number of particles reaching the target cerebralarteries (e.g., of step 311) with respect to the total number ofreleased particles. Alternately or in addition, step 327 may includetracking the path of a single particle traveling through the patient'scirculatory system. In one embodiment, step 329 may include storing thedestination probability of the embolus (e.g., to an electronic storagemedium and/or RAM).

FIGS. 4A-4C depict exemplary methods of predicting peripheral-relatedrisks and evaluating treatment options associated with theperipheral-related risks, according to an exemplary embodiment. Themethods of FIGS. 4A-4C may include identifying embolic sources using CFDanalysis in conjunction with patient-specific images of peripheralarteries and veins. Identifying the embolic sources in peripheralarteries and veins may help determine treatment to reduce a patient'srisk of kidney embolism, pulmonary (e.g., right-sided or venous)embolism, and/or mesenteric or lower extremity embolism.

FIG. 4A is a flowchart of an exemplary method 400 of determiningdestination(s) in peripheral vessels for an embolus dislodged from alocation in patient's vasculature, according to an exemplary embodiment.The method of FIG. 4A may be performed by server systems 106, based oninformation, images, and data received from physicians 102 and/or thirdparty providers 104 over electronic network 101.

In one embodiment, steps 401-407 may include receiving variousinformation on a patient. Steps 401 and 403 may be similar to steps 301and 303, respectively. For example, step 401 may include receiving thepatient's medical history, including any inherited or acquiredhypercoagulable state that may affect thrombotic risk. For instance,patient medical history may include the patient's family medicalhistory, as well as the patient's prior history of deep venousthrombosis or pulmonary embolism, factor V Leiden, cancer, and/or recenttrauma or surgery. Step 403 may include receiving information onmedications that the patient may be using that may affect thromboticrisk. Examples of such medications may include: Aspirin, Clopidogrel,Coumadin/Warfarin, Heparin, etc.

Step 405 may include receiving the patient's physiologic conditionsand/or a model of the patient's anatomy (e.g., including at least aportion of the patient's circulatory system). The model of the patient'sanatomy may include a representation of the patient's aortic arch,coronary, renal, mesenteric, and/or pulmonary and peripheral arteriesand veins. Alternately or in addition, the model of the patient'sanatomy may include a 3D mesh model (e.g., obtained via segmentation ofperipheral, cardiac and/or abdominal CT images) and/or apatient-specific artery model combined with a generic circulatory model(e.g., of a coronary, aortic arch, etc.) based on a population average.Patient physiologic conditions may include, for example: age, sex, bloodpressure/heart rate under rest/exercise conditions, physical activity(e.g., exercise intensity), sedentary time per day, obesity, etc.

In one embodiment, step 407 may include receiving one or more locationsof interest in the patient's anatomy (e.g., a plaque, pathological area,location of possible vascular shedding, etc.). For example, thelocations of interest may be received from an electronic storage medium.Alternately or in addition, step 407 may include identifying one or morelocations of interest in the patient's anatomy. For example, thelocations of interest may be identified via invasive and/or noninvasiveimaging (e.g., CT, MRI, IVUS, Doppler ultrasound, etc.). In oneexemplary case, step 407 may include identifying the one or morelocations of interest by detecting atherosclerotic plaques in apatient's vessel(s). Step 407 may further include storing the identifiedlocation(s) of interest electronically (e.g., via an electronic storagemedium, RAM, etc.). Information regarding a location of interest in thepatient may include, for example, information on the presence andseverity of atherosclerotic carotid artery disease, cardiac disease,venous disease, and/or arterial dissection in the patient's anatomy. Forinstance, the presence and severity of cardiac disease may includeinformation on any heart condition(s), disorders, or irregularities apatient may have, e.g., atrial fibrillation (and/or left atrialappendage activity), performance of one or more prosthetic heart valves,patent foramen ovale, acute myocardial infarction, and/or leftventricular dysfunction.

In one embodiment, step 409 may include determining blood flowcharacteristics (e.g., using a computational fluid dynamics simulationand/or approximation). Step 409 may further include determining acirculatory destination probability of a dislodged embolus (e.g., anembolus dislodged from a culprit embolic source, including the patient'saorta, carotid, or heart). In one embodiment, determining thecirculatory destination probability, based on an embolus source, mayinclude performing Lagrangian particle tracking. An exemplarycomputational fluid dynamics analysis for determining blood flowcharacteristics and circulatory destination probability, is described inthe method of FIG. 4C.

In one embodiment, step 409 may further include outputting and/orstoring the circulatory destination probability, e.g., to an electronicstorage medium or display. In some instances, the circulatorydestination probability may be stored such that the probability isassociated with the location of interest, wherein the location ofinterest may be identified as a potential culprit embolus source.Furthermore, step 409 may include determining locations vulnerable toembolism, based on the determined circulatory destination probabilities.

FIG. 4B is a flowchart of an exemplary method 410 of determining andevaluating source locations of embolism in a patient's peripheralvasculature, according to an exemplary embodiment. The method of FIG. 4Bmay be performed by server systems 106, based on information, images,and data received from physicians 102 and/or third party providers 104over electronic network 101.

In one embodiment, step 411 may include receiving one or moredestination locations of interest in the patient's vasculature. Forexample, for peripheral-related risks (e.g., kidney embolism, pulmonary(e.g., right-sided or venous) embolism, and/or mesenteric or lowerextremity embolism), destination locations of interest may include oneor more pulmonary, renal, peripheral, or femoral arteries. In otherwords, emboli presence in the pulmonary, renal, or femoral arteries maypresent risk of pulmonary (e.g., right-sided or venous) embolism, kidneyembolism, lower extremity embolism, and/or mesenteric embolism,respectively.

In one embodiment, step 413 may include determining stored and/orcomputed circulatory destination probabilities associated with areceived destination location (e.g., of step 409). For example, thereceived destination locations may be identified as locations vulnerableto embolism.

In one embodiment, step 415 may include determining source location(s)associated with a determined circulatory destination probability (e.g.,of step 413), and thereby associated with a received destinationlocation (e.g., of step 411). For example, step 415 may includeretrieving stored embolic sources (e.g., from method 400), based oncirculatory destination probabilities for the one or more destinationsof interest. For example, an embolic source may include one or more ofthe one or more locations of interest (e.g., from step 407).

In one embodiment, step 417 may include generating various outputsincluding, for example, destination/vulnerable location(s) (e.g., ofstep 411) and/or source location(s) (e.g., of step 415). For example,step 417 may include outputting a representation including one or moredestination probabilities and/or embolic paths in a patient arterymodel. In one case, such a representation of the artery model mayinclude visual indication (e.g., highlighting) at vulnerable embolismlocation(s) and/or at embolic source(s) associated with the patient'svulnerable embolism location(s). In one embodiment, the representationincluding the artery model may be stored to an electronic storagemedium. Alternately or in addition, step 417 may include generating arepresentation or display showing selected location(s) of embolicsources.

For example, the representation or display may include a user interfacefor a user (e.g., a health care provider) to select one or morelocations and/or embolic sources in the received model of the patient'sartery model or the representation of the artery model. Therepresentation or display may then include numerical or color indicatorsshowing risk of embolism or destination probabilities. The outputs ofstep 417 may be made accessible to physicians evaluating potentialtreatments to reduce the risk or number of pulmonary, kidney,mesenteric, or lower extremity embolisms.

In one embodiment, step 419 may include analysis of the output of step417, e.g., associating destination probabilities with various locationsin the patient artery model and effects of emboli presence at thedestinations. For example, step 419 may include outputting a patientrisk of pulmonary, kidney, mesenteric, or lower extremity embolisms,based on the computed risk of embolism at one or more locations in thepatient artery model.

FIG. 4C is a flowchart of an exemplary method 420 of determining bloodflow characteristics and circulatory destination probability of adislodged embolus for peripheral-related risks, according to anexemplary embodiment. The method of FIG. 4C may be performed by serversystems 106, based on information, images, and data received fromphysicians 102 and/or third party providers 104 over electronic network101.

In one embodiment, step 421 may include computing a velocity field ofblood flow in the patient's anatomy (e.g., the patient's heart,coronary, cerebral, carotid, and aortic arch). Computing the velocityfield of blood flow may include computationally solving Navier-Stokesequations under the received patient physiologic conditions. Forexample, a computational model may include venous circulation as well asarterial circulation. Venous circulation may be modeled by includingcollapsibility of veins due to the effect of external forces (e.g.,gravity, external pressure, etc.).

In one embodiment, step 423 may include simulating the path of adislodged embolus through the patient's circulatory system based on thecomputed velocity field. For example, step 423 may include may includeinjecting particles virtually in the received or identified potentialembolic sources (e.g., received or identified femoral veins,atheromatous plaque in aorta, and/or heart valves from step 415).

Step 425 may include determining a trajectory of particles (e.g., bysolving the ordinary differential equation of {dot over (x)}(t)=u(x,t);x(t₀)=x₀ using an appropriate numerical method, where u(x,t) is thevelocity field and x(t) is the location of particle at time t). The sizeand number of particles may be determined by non-invasive imaging (e.g.,ultrasound) or estimated by the disease severities of embolic sources.

Step 427 may include determining the destination probability of anembolus (e.g., by computing the ratio of the number of particlesreaching target pulmonary, renal, or femoral arteries (e.g., of step411) with respect to the total number of released particles). Step 429may include storing the destination probability of the embolus (e.g., toan electronic storage medium and/or RAM).

FIG. 5A is a flowchart of an exemplary method 500 of assessing orassigning a risk of potential emboli dislodgement associated with aninvasive procedure, according to an exemplary embodiment. The method ofFIG. 5A may be performed by server systems 106, based on information,images, and data received from physicians 102 and/or third partyproviders 104 over electronic network 101. FIGS. 5A-5C may includeevaluating a risk of embolism that may be associated with one or moreinvasive procedures. Emboli may be dislodged during invasive procedures,including endovascular procedures (e.g., angiography, stentingprocedures, transcatheter valve procedures (e.g., transcatheter aorticvalve implantation (TAVI)), and/or revascularization of coronary,carotid, and/or peripheral arteries. For example, method 500 may includeproviding a probability of emboli dislodgement from potential embolicsources, along the trajectory of invasive procedures.

In one embodiment, steps 501-507A may include receiving variousinformation on a patient. Steps 501 may be similar to steps 301 and 401,and step 503 may be similar to steps 303 and 403. For example, step 501may include receiving the patient's medical history, including anyinherited or acquired hypercoagulable state that may affect thromboticrisk. For instance, patient medical history may include the patient'sfamily medical history, as well as the patient's prior history of deepvenous thrombosis or pulmonary embolism, factor V Leiden, cancer, and/orrecent trauma or surgery. Step 503 may include receiving information onmedications that the patient may be using that may affect thromboticrisk. Examples of such medications may include: Aspirin, Clopidogrel,Coumadin/Warfarin, Heparin, etc.

In one embodiment, step 505 may include receiving the patient'sphysiologic conditions and/or a model of the patient's anatomy (e.g.,including at least a portion of the patient's circulatory system). Themodel of the patient's anatomy may include a representation of thepatient's aortic arch, coronary, carotid, and/or cerebral arteries andveins. Alternately or in addition, the model of the patient's anatomymay include a 3D mesh model (e.g., obtained via segmentation of cardiacand/or head CT images) and/or a patient-specific artery model combinedwith a generic circulatory model (e.g., of a coronary, aortic arch,etc.) based on a population average. Patient physiologic conditions mayinclude, for example: age, sex, blood pressure/heart rate underrest/exercise conditions, physical activity (e.g., exercise intensity),sedentary time per day, obesity, etc. Further data on patientinformation or modeling the patient's anatomy/physical state may includeany data on extrinsic forces and/or conditions that may act on an areaof interest of the patient's body, including body position (e.g.,supine, standing, standing on head, flexion, extension, etc.) and/orextreme conditions (e.g., acceleration or deceleration, sportingconditions, g-forces, deep diving, valsalva, shoveling snow, pregnancy,etc.). Any extrinsic forces that may affect the circulatory system maybe taken into account for step 505, since any of these factors may be atriggering event for an embolus.

In one embodiment, step 507A may include receiving one or more locationsof interest in the patient's anatomy (e.g., a plaque, pathological area,location of possible vascular shedding, etc.). For example, thelocations of interest may be received from an electronic storage medium.Alternately or in addition, step 507A may include identifying one ormore locations of interest in the patient's anatomy. For example, thelocations of interest may be identified via invasive and/or noninvasiveimaging (e.g., CT, MRI, IVUS, Doppler ultrasound, etc.). In oneexemplary case, step 507A may include identifying the one or morelocations of interest by detecting atherosclerotic plaques in apatient's vessel(s). Identified location(s) of interest may be storedelectronically (e.g., via an electronic storage medium, RAM, etc.).Information about a location of interest in the patient may include, forexample, information on the presence and/or severity of atheroscleroticcarotid artery disease, intracranial stenosis, cardiac disease, venousdisease, and/or arterial dissection in the patient's anatomy. Forinstance, presence and severity of cardiac disease may includeinformation on any heart condition(s), disorder(s), or irregularities apatient may have, e.g., atrial fibrillation (and/or left atrialappendage activity), performance of one or more prosthetic heart valves,patent foramen ovale, acute myocardial infarction, and/or leftventricular dysfunction.

In one embodiment, step 507B may include determining a potentialtrajectory of an invasive procedure in the model of the patient'sanatomy. The potential trajectory may be determined, based on the one ormore received and/or identified locations of interest (of step 507A).Exemplary trajectories may include guide-wire, catheter, or pressurewire trajectories along a superficial femoral artery, aortic arch, etc.

In one embodiment, step 509 may include determining blood flowcharacteristics (e.g., using computational fluid dynamics (orapproximation)). Step 509 may further include determining a circulatorydestination probability of a dislodged embolus, e.g., an embolusdislodged from a culprit embolic source along the trajectory of theinvasive procedure (e.g., from step 507B). In one embodiment,determining the circulatory destination probability, based on an embolussource, may include performing Lagrangian particle tracking. Anexemplary computational fluid dynamics analysis for determining bloodflow characteristics and circulatory destination probability isdescribed in the method of FIG. 5C.

In one embodiment, step 509 may further include outputting and/orstoring the circulatory destination probability, e.g., to an electronicstorage medium or display. In some instances, the circulatorydestination probability may be stored such that the probability isassociated with the location of interest, wherein the location ofinterest may be identified as a potential source location for anembolus. Furthermore, step 509 may include determining locationsvulnerable to embolism, based on the determined circulatory destinationprobabilities.

FIG. 5B is a block diagram of an exemplary method 510 of determining andevaluating source locations of embolism associated with invasiveprocedures, according to an exemplary embodiment. The method of FIG. 5Bmay be performed by server systems 106, based on information, images,and data received from physicians 102 and/or third party providers 104over electronic network 101.

In one embodiment, step 511 may include receiving one or moredestination locations of interest in the patient's vasculature. Forexample, destination locations of interest for cerebral-related risks(e.g., stroke, TIA, cognitive impairment, or postoperative cerebralischemia) may include one or more cerebral arteries. Forperipheral-related risks (e.g., kidney embolism, pulmonary (e.g.,right-sided or venous) embolism, and/or mesenteric or lower extremityembolism), destination locations of interest may include one or morepulmonary, renal, peripheral, or femoral arteries.

In one embodiment, step 513 may include determining stored and/orcomputed circulatory destination probabilities associated with areceived destination location (e.g., of step 509). For example, thereceived destination locations may be identified as locations vulnerableto embolism.

In one embodiment, step 515 may include determining source location(s)associated with a computed circulatory destination probability (e.g., ofstep 513), and thereby associated with a received destination location(e.g., of step 515). For example, step 515 may include retrieving storedembolic sources (e.g., from method 500), based on circulatorydestination probabilities for the one or more destinations of interest.For example, an embolic source may include one or more of the one ormore locations of interest (e.g., from step 507A). In one embodiment,step 515 may involve determining associations between the computeddestination probabilities and embolic sources located along a trajectoryof the invasive procedure (e.g., from step 507B). For example, step 515may include finding or identifying, of the embolic sources associatedwith the computed destination probabilities from step 509, a subset ofembolic sources that may be located along one or more trajectories ofthe invasive procedure from step 507B.

In one embodiment, step 517 may include generating various outputsincluding, for example, destination/vulnerable location(s) (e.g., ofstep 509) and/or source location(s) (e.g., of step 515). For example,step 517 may include outputting a representation including one or moredestination probabilities in a patient artery model. In one case, such arepresentation of the artery model may include a visual indication(e.g., highlighting) at one or more trajectories of an invasiveprocedure, at vulnerable embolism location(s), at possible locationsalong an embolus's path through the patient's circulatory system, and/orat embolic source(s) associated with the patient's cerebral orvulnerable embolism location(s). In one embodiment, the representationincluding the artery model may be stored to an electronic storagemedium. Alternately or in addition, step 517 may include generating arepresentation or display showing selected location(s) of embolicsources.

For example, the representation or display may include a user interfacefor a user (e.g., a health care provider) to compare the efficacy of oneor more potential treatments in reducing risk associated with invasiveprocedures. The representation or display may then include numerical orcolor indicators showing risk of embolism or destination probabilities.The outputs of step 517 may be made accessible to physicians evaluatingpotential treatments to reduce the risk or number of embolisms resultingfrom invasive procedures.

In one embodiment, step 519 may include analysis of the output of step517, e.g., associating destination probabilities with various locationsin the patient artery model and effects of emboli presence at thedestinations. For example, step 519 may include outputting a patientrisk of emboli dislodgement and/or harmful emboli dislodgement that mayoccur as a result of an invasive procedure performed on the patient'svasculature. Alternately or in addition, step 519 may include outputtinga patient risk of embolism(s) and/or harmful embolism(s) that may occuras a result of an invasive procedure performed on the patient'svasculature. Step 519 may include generating a recommendation regardingthe one or more potential treatments for reducing risk associated withinvasive procedures.

FIG. 5C is a flowchart of an exemplary method 520 of determining bloodflow characteristics and circulatory destination probability of adislodged embolus related to invasive procedures, according to anexemplary embodiment. The method of FIG. 5C may be performed by serversystems 106, based on information, images, and data received fromphysicians 102 and/or third party providers 104 over electronic network101.

In one embodiment, step 521 may include computing a velocity field ofblood flow in the patient's anatomy (e.g., the patient's heart,coronary, cerebral, carotid, renal, femoral, and/or aortic arch).Computing the velocity field of blood flow may include computationallysolving Navier-Stokes equations under the received patient physiologicconditions. For example, a computational model may include venouscirculation as well as arterial circulation. Venous circulation may bemodeled by including collapsibility of veins due to the effect ofexternal forces (e.g., gravity, external pressure, etc.).

In one embodiment, step 523 may include simulating the path of adislodged embolus through the patient's circulatory system based on thecomputed velocity field. For example, step 523 may include injectingparticles virtually in the received or identified potential embolicsources (e.g., received or identified carotid stenosis, atheromatousplaque in aorta, heart valves, etc. from step 515).

In one embodiment, step 525 may include determining the trajectory ofparticles (e.g., by solving the ordinary differential equation of {dotover (x)}(t)=u(x,t); x(t₀)=x₀ using an appropriate numerical method,where u(x,t) is the velocity field and x(t) is the location of particleat time t). The size and number of particles may be determined bynon-invasive imaging (e.g., ultrasound) or estimated by the diseaseseverities of embolic sources.

In one embodiment, step 527 may include determining the destinationprobability of an embolus (e.g., by computing the ratio of the number ofparticles reaching the target cerebral or peripheral arteries withrespect to the total number of released particles). Step 529 may includestoring the probability of the destination (e.g., to an electronicstorage medium and/or RAM).

Embolisms may form from emboli originating from various sources in apatient's vasculature and various factors contributing to the patient'sblood flow. The present disclosure includes systems and methods forpredicting embolisms based on a circulatory destination probability ofan embolus traveling through a patient's bloodstream. At the same time,the systems and methods provide determinations of the level of harmintroduced by an embolism traveling through a patient's bloodstream.Accordingly, the disclosed systems and methods may provide patient riskassessments and treatment plans related to embolisms, based oncirculatory destination probabilities of emboli.

Other embodiments of the invention will be apparent to those skilled inthe art from consideration of the specification and practice of theinvention disclosed herein. It is intended that the specification andexamples be considered as exemplary only, with a true scope and spiritof the invention being indicated by the following claims.

What is claimed is:
 1. A computer-implemented method of determining apatient risk assessment or treatment plan based on emboli dislodgementand destination, the method comprising: receiving a patient-specificanatomic model generated from patient-specific imaging of at least aportion of a patient's vasculature; determining or receiving a locationof interest in the patient-specific anatomic model of the patient'svasculature; using a computing processor for calculating blood flowthrough the patient-specific anatomic model to determine blood flowcharacteristics through at least the portion of the patient'svasculature of the patient-specific anatomic model downstream from thelocation of interest; and using a computing processor for particletracking through the simulated blood flow to determine a destinationprobability of an embolus originating from the location of interest inthe patient-specific anatomic model, based on the determined blood flowcharacteristics.
 2. The computer-implemented method of claim 1, furthercomprising: determining, in the patient's vasculature, a location of aplaque, a location meeting a predetermined criteria of pathology, or alocation of possible vascular shedding; and determining or receiving thelocation of interest in the patient-specific anatomic model based on thedetermined location of plaque, determined location meeting thepredetermined criteria of pathology, or the location of possiblevascular shedding.
 3. The computer-implemented method of claim 1,further comprising: determining a target location in the patient'svasculature, wherein the destination probability is a probability thatthe embolus traveling through the patient's circulatory system reachesthe target location, based on the determined blood flow characteristics.4. The computer-implemented method of claim 1, further comprising:determining a vulnerable location of embolism based on the determineddestination probability.
 5. The computer-implemented method of claim 1,further comprising: determining an association between a target locationin the patient-specific anatomic model, the destination probability, andthe location of interest.
 6. The computer-implemented method of claim 5,further comprising: determining, for a second location of interest, asecond destination probability of an embolus associated with the secondlocation of interest; determining an association between the targetlocation in the patient-specific anatomic model, the destinationprobability, and the second location of interest; comparing theassociation between the target location in the patient-specific anatomicmodel, the destination probability, and the location of interest, andthe association between the target location in the patient-specificanatomic model, the destination probability, and the second location ofinterest; and determining generating a display or a treatmentrecommendation based on the comparison.
 7. The computer-implementedmethod of claim 1, further comprising: determining patient risk of adisease, based on the destination probability of the embolus, whereinthe patient risk of the disease includes patient risk of a diseaseassociated with a location in the patient's vasculature.
 8. Thecomputer-implemented method of claim 7, wherein the risk of diseaseincludes a risk of embolism, cognitive impairment, stroke, transientischemic attack, pulmonary embolism, renal embolism, or a combinationthereof.
 9. A system for determining a patient risk assessment ortreatment plan based on emboli dislodgement and destination, the systemcomprising: a data storage device storing instructions for determining apatient risk assessment or treatment plan based on emboli dislodgementand destination; and a processor configured to execute the instructionsto perform a method including: receiving a patient-specific anatomicmodel generated from patient-specific imaging of at least a portion of apatient's vasculature; determining or receiving a location of interestin the patient-specific anatomic model of the patient's vasculature;using a computing processor for calculating blood flow through thepatient-specific anatomic model to determine blood flow characteristicsthrough at least the portion of the patient's vasculature of thepatient-specific anatomic model downstream from the location ofinterest; and using a computing processor for particle tracking throughthe simulated blood flow to determine a destination probability of anembolus originating from the location of interest in thepatient-specific anatomic model, based on the determined blood flowcharacteristics.
 10. The system of claim 9, wherein the system isfurther configured for: determining, in the patient's vasculature, alocation of a plaque, a location meeting a predetermined criteria ofpathology, or a location of possible vascular shedding; and determiningor receiving the location of interest in the patient-specific anatomicmodel based on the determined location of plaque, determined locationmeeting the predetermined criteria of pathology, or the location ofpossible vascular shedding.
 11. The system of claim 9, wherein thesystem is further configured for: determining a target location in thepatient's vasculature, wherein the destination probability is aprobability that the embolus traveling through the patient's circulatorysystem reaches the target location, based on the determined blood flowcharacteristics.
 12. The system of claim 9, wherein the system isfurther configured for: determining a vulnerable location of embolismbased on the determined destination probability.
 13. The system of claim9, wherein the system is further configured for: determining anassociation between a target location in the patient-specific anatomicmodel, the destination probability, and the location of interest. 14.The system of claim 13, wherein the system is further configured for:determining, for a second location of interest, a second destinationprobability of an embolus associated with the second location ofinterest; determining an association between the target location in thepatient-specific anatomic model, the destination probability, and thesecond location of interest; comparing the association between thetarget location in the patient-specific anatomic model, the destinationprobability, and the location of interest, and the association betweenthe target location in the patient-specific anatomic model, thedestination probability, and the second location of interest; anddetermining generating a display or a treatment recommendation based onthe comparison.
 15. The system of claim 9, wherein the system is furtherconfigured for: determining patient risk of a disease, based on thedestination probability of the embolus, wherein the patient risk of thedisease includes patient risk of a disease associated with a location inthe patient's vasculature.
 16. The system of claim 15, wherein the riskof disease includes a risk of embolism, cognitive impairment, stroke,transient ischemic attack, pulmonary embolism, renal embolism, or acombination thereof.
 17. A non-transitory computer readable medium foruse on a computer system containing computer-executable programminginstructions for performing a method of determining a patient riskassessment or treatment plan based on emboli dislodgement anddestination, the method comprising: receiving a patient-specificanatomic model generated from patient-specific imaging of at least aportion of a patient's vasculature; determining or receiving a locationof interest in the patient-specific anatomic model of the patient'svasculature; using a computing processor for calculating blood flowthrough the patient-specific anatomic model to determine blood flowcharacteristics through at least the portion of the patient'svasculature of the patient-specific anatomic model downstream from thelocation of interest; and using a computing processor for particletracking through the simulated blood flow to determine a destinationprobability of an embolus originating from the location of interest inthe patient-specific anatomic model, based on the determined blood flowcharacteristics.
 18. The non-transitory computer readable medium ofclaim 17, the method further comprising: determining, in the patient'svasculature, a location of a plaque, a location meeting a predeterminedcriteria of pathology, or a location of possible vascular shedding; anddetermining or receiving the location of interest in thepatient-specific anatomic model based on the determined location ofplaque, determined location meeting the predetermined criteria ofpathology, or the location of possible vascular shedding.
 19. Thenon-transitory computer readable medium of claim 17, the method furthercomprising: determining a target location in the patient's vasculature,wherein the destination probability is a probability that the embolustraveling through the patient's circulatory system reaches the targetlocation, based on the determined blood flow characteristics.
 20. Thenon-transitory computer readable medium of claim 19, the method furthercomprising: determining a vulnerable location of embolism based on thedetermined destination probability.